In livestock farming, it is important to monitor regularly the weight of living (or in vivo) domesticated animals e.g., pigs, etc., to give an indication of the animals' rate of growth. An animal's growth rate is important as it can determine the profitability of a farming enterprise, especially where meat production is concerned. The weight and health of the livestock can also be related back to the size, shape and condition of the animal. The study of the relationship between shape and weight is referred to as allometry.
Traditionally, weighing livestock is performed manually on-farm using mechanical or electronic scales. This practice is very labour intensive, time consuming and potentially dangerous. Furthermore, the procedures associated with manual weighing are stressful for the animals and the stockmen involved. Computer systems can be used in some conditions for determining information about animals based on images of the animals. Computer-based image analysis can predict a live animal's weight based on body measurements extracted from an image containing an animal (also referred to as an animal image). These body measurements can then be used to predict the animal's weight.
Existing image analysis systems and methods, have insufficient weight prediction accuracy, image capture reliability, and feature extraction reliability, particularly in farming or livestock-handling environments where lighting can be non-uniform (variable).
Furthermore, existing weight estimation methods can be too slow for high-throughput farming environments where many measurements are required.
It is desired to address or ameliorate one or more disadvantages or limitations associated with the prior art, e.g., as described above, or to at least provide a useful alternative.